Case-Based Recommendation Barry Smyth The School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland Changing worlds Ltd South County Business Park, Leopardstown, Dublin 18. Ireland. Barry. Smyth@ucd. ie paces.a good example is when they are used to help users to access onlinepiox o Abstract. Recommender systems try to help users access complex informati uct catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences Case-based recommendation is a form of content -based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features(e. g, price, colour, make etc. ) These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommenda- tions and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic ap- proach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have ed to more powerful and flexible recommender systems 11.1 Introduction Recently I wanted to buy a new digital camera. I had a vague idea of what I wanted-a 6 mega-pixel digital SLR from a good manufacturer-but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or nar igate through their product catalog by choosing from a series of static features(e.g manufacturer, camera type, resolution, level of zoom etc. ) Each time I selected a fea- ture I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query(e. g. " digital slr 6 mega-pixels") and presented me with a list of results which I could then browse at my leisure Both of these access options were helpful in different ways-in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces--however neither provided Brusilovsky, A. Kobsa, and w. Nejdl(Eds ) The Adaptive Web, LNCS 4321, Pp. 342-376, 2007. C Springer-Verlag Berlin Heidelberg 2007
11 Case-Based Recommendation Barry Smyth1,2 1 The School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland 2 ChangingWorlds Ltd. South County Business Park, Leopardstown, Dublin 18, Ireland. Barry.Smyth@ucd.ie Abstract. Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems. 11.1 Introduction Recently I wanted to buy a new digital camera. I had a vague idea of what I wanted—a 6 mega-pixel digital SLR from a good manufacturer—but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or navigate through their product catalog by choosing from a series of static features (e.g., manufacturer, camera type, resolution, level of zoom etc.). Each time I selected a feature I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query (e.g. “digital slr, 6 mega-pixels”) and presented me with a list of results which I could then browse at my leisure. Both of these access options were helpful in different ways—in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces—however neither provided P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 342–376, 2007. c Springer-Verlag Berlin Heidelberg 2007
11 Case-Based Recommendation 343 me with the flexibility I really sought. For a start, all of the stores I tried tended to slavishly respect my queries. This was especially noticeable when no results could be returned to satisfy my stated needs; this is often referred to as stonewalling [17]. For instance, looking for a 6 mega-pixel digital SiR for under $200 proved fruitless- unsurprising perhaps to those in the know-and left me with no choice but to start my search again. This was especially frustrating when there were many cameras that were similar enough to my query to merit suggestion. Moreover, stonewalling is further compounded by a diversity problem: I was frequently presented with sets of product hat were all very similar to each other thus failing to offer me a good set of alternatives At other times I would notice a camera that was almost perfect, aside from perhaps one or two features, but it was usually difficult to provide this form of feedback directly This feedback problem prevented me from requesting"another camera like this one bu with more optical zoom and/or a lower price", for instance In all, perhaps one of the most frustrating aspects of my search was the apparent inability of most online stores to learn anything about my preferences over time. In my opinion shopping for an expensive item such as a digital camera is an exercise in pa tience and deliberation, and one that is likely to involve many return visits to particula online stores. Unfortunately, despite the fact that I had spent a significant time and effort searching and browsing for cameras during previous visits none of the stores I visited had any facility to remember my previous interactions or preferences. For instance, my reluctance to purchase a very expensive cameraL never accepted recommendations for cameras above $1000--should have been recognised and factored into the stores recommendations, but it was not. As a result many of my interactions turned out to be requests for less expensive suggestions. This preference problem meant that starting my searches from scratch became a regular feature of these visits sA Recommender systems are designed to address many of the prol blems mentioned above, and more besides, by offering users a more intelligent approach to navigat- ng and searching complex information spaces. They have been especially useful in many e-commerce domains with many stores using recommendation technologies to help convert browsers into buyers by providing intelligent and timely sales support and product suggestions; see for example Chapter 16 of this book [38] for a survey of recommendation techniques in an e-commerce setting. One of the key features of many recommendation technologies is the ability to consider the needs and preferences of the individual when it comes to generating personalized recommendations or sug- gestions. We will return to this issue later in this chapter but also refer the interested reader to related work on the development of personalization technologies. For exam ple, Chapters 2[35] and 4 [44] of this book consider different approaches to learnin and modeling the preferences of users while Chapters 3 [62], 6[61], and 18 [8] of this book consider different ways in which user models may be harnessed to provide users with more personalized access to online information and services. Indeed, while many recommendation and personalization technologies focus on the needs of the individual some researchers have begun to consider group recommendation scenarios where the potentially competing preferences of a number of individuals need to be considered see for example Chapter 20 [41]of this book
11 Case-Based Recommendation 343 me with the flexibility I really sought. For a start, all of the stores I tried tended to slavishly respect my queries. This was especially noticeable when no results could be returned to satisfy my stated needs; this is often referred to as stonewalling [17]. For instance, looking for a 6 mega-pixel digital SLR for under $200 proved fruitless— unsurprising perhaps to those ‘in the know’—and left me with no choice but to start my search again. This was especially frustrating when there were many cameras that were similar enough to my query to merit suggestion. Moreover, stonewalling is further compounded by a diversity problem: I was frequently presented with sets of products that were all very similar to each other thus failing to offer me a good set of alternatives. At other times I would notice a camera that was almost perfect, aside from perhaps one or two features, but it was usually difficult to provide this form of feedback directly. This feedback problem prevented me from requesting “another camera like this one but with more optical zoom and/or a lower price”, for instance. In all, perhaps one of the most frustrating aspects of my search was the apparent inability of most online stores to learn anything about my preferences over time. In my opinion shopping for an expensive item such as a digital camera is an exercise in patience and deliberation, and one that is likely to involve many return visits to particular online stores. Unfortunately, despite the fact that I had spent a significant time and effort searching and browsing for cameras during previous visits none of the stores I visited had any facility to remember my previous interactions or preferences. For instance, my reluctance to purchase a very expensive camera—I never accepted recommendations for cameras above $1000—should have been recognised and factored into the store’s recommendations, but it was not. As a result many of my interactions turned out to be requests for less expensive suggestions. This preference problem meant that starting my searches from scratch became a regular feature of these visits. Recommender systems are designed to address many of the problems mentioned above, and more besides, by offering users a more intelligent approach to navigating and searching complex information spaces. They have been especially useful in many e-commerce domains with many stores using recommendation technologies to help convert browsers into buyers by providing intelligent and timely sales support and product suggestions; see for example Chapter 16 of this book [38] for a survey of recommendation techniques in an e-commerce setting. One of the key features of many recommendation technologies is the ability to consider the needs and preferences of the individual when it comes to generating personalized recommendations or suggestions. We will return to this issue later in this chapter but also refer the interested reader to related work on the development of personalization technologies. For example, Chapters 2 [35] and 4 [44] of this book consider different approaches to learning and modeling the preferences of users while Chapters 3 [62], 6 [61], and 18 [8] of this book consider different ways in which user models may be harnessed to provide users with more personalized access to online information and services. Indeed, while many recommendation and personalization technologies focus on the needs of the individual, some researchers have begun to consider group recommendation scenarios where the potentially competing preferences of a number of individuals need to be considered; see for example Chapter 20 [41] of this book
Recommendation techniques come in two basic flavours. Collaborative filtering ap hes rely on the availability of user ratings information(e. g. "John likes items A, Id c but dislikes items e and F"and make suggestions for a target user based on the items that similar users have liked in the past, without relying on any information bout the items themselves other than their ratings; see Chapter 9[83] of this book for a more detailed account of collaborative filtering approaches. In contrast content-based techniques rely on item descriptions and generate recommendations from items that are similar to those the target user has liked in the past, without directly relying on the pref- erences of other users; see Chapter 10 [69]of this book for a detailed account of pure content-based approaches Case-based recommenders implement a particular style of content-based recom mendation that is very well suited to many product recommendation scenarios; see also [16]. They rely on items or products being represented in a structured way using a well defined set of features and feature values: for instance. in a travel recommender a particular vacation might be presented in terms of its price, duration, accommodation, location, mode of transport, etc. In turn the availability of similarity knowledge makes it possible for case-based recommenders to make fine-grained judgments about the sim- ilarities between items and queries for informing high-quality suggestions to the user. Case-based recommender systems are the subject of this chapter, where we will draw on a range of examples from a variety of recommender systems, both research proto- types and deployed applications. We will explain their origins in case-based reasoning research [1, 31, 46, 101] and their basic mode of operation as recommender systems In particular, we will look at how case-based recommenders deal with the issues high lighted above in terms of their approach to selection similarity, recommendation diver- sity, and the provision of flexible feedback options. In addition we will consider the use of case-based recommendation techniques to produce suggestions that are personalized for the needs of the individual user and in this way present case-based approaches as one important solution for Web personalization problems: see also Chapters 2351, 3 [62], and 16 [38] in this book for related work in the area of Web personalization 11.2 Towards Case-Based recommendation Case-based recommender systems have their origins in case-based reasoning(CBR) techniques [1, 46, 101, 48, 99]. Early case-based reasoning systems were used in a variety of problem solving and classification tasks and can be distinguished from more traditional problem solving techniques by their reliance on concrete experiences instead of problem solving knowledge in the form of codified rules and strong domain models Case-based reasoning systems rely on a database(or case base)of past problem solving experiences as their primary source of problem-solving expertise. Each case is typically made up of a specification part, which describes the problem at hand, and a solution part, which describes the solution used to solve this problem. New problems are solved by retrieving a case whose specification is similar to the current target problem and then adapting its solution to fit the target situation. For example, CLAVIER [39]is a case-based reasoning system used by Lockheed to assist in determining the layout of materials to be cured in an autoclave (i.e, a large convection oven used, in this
344 B. Smyth Recommendation techniques come in two basic flavours. Collaborative filtering approaches rely on the availability of user ratings information (e.g. “John likes items A, B and C but dislikes items E and F” and make suggestions for a target user based on the items that similar users have liked in the past, without relying on any information about the items themselves other than their ratings; see Chapter 9 [83] of this book for a more detailed account of collaborative filtering approaches. In contrast content-based techniques rely on item descriptions and generate recommendations from items that are similar to those the target user has liked in the past, without directly relying on the preferences of other users; see Chapter 10 [69] of this book for a detailed account of pure content-based approaches. Case-based recommenders implement a particular style of content-based recommendation that is very well suited to many product recommendation scenarios; see also [16]. They rely on items or products being represented in a structured way using a well defined set of features and feature values; for instance, in a travel recommender a particular vacation might be presented in terms of its price, duration, accommodation, location, mode of transport, etc. In turn the availability of similarity knowledge makes it possible for case-based recommenders to make fine-grained judgments about the similarities between items and queries for informing high-quality suggestions to the user. Case-based recommender systems are the subject of this chapter, where we will draw on a range of examples from a variety of recommender systems, both research prototypes and deployed applications. We will explain their origins in case-based reasoning research [1, 31, 46, 101] and their basic mode of operation as recommender systems. In particular, we will look at how case-based recommenders deal with the issues highlighted above in terms of their approach to selection similarity, recommendation diversity, and the provision of flexible feedback options. In addition we will consider the use of case-based recommendation techniques to produce suggestions that are personalized for the needs of the individual user and in this way present case-based approaches as one important solution for Web personalization problems; see also Chapters 2 [35], 3 [62], and 16 [38] in this book for related work in the area of Web personalization. 11.2 Towards Case-Based Recommendation Case-based recommender systems have their origins in case-based reasoning (CBR) techniques [1, 46, 101, 48, 99]. Early case-based reasoning systems were used in a variety of problem solving and classification tasks and can be distinguished from more traditional problem solving techniques by their reliance on concrete experiences instead of problem solving knowledge in the form of codified rules and strong domain models. Case-based reasoning systems rely on a database (or case base) of past problem solving experiences as their primary source of problem-solving expertise. Each case is typically made up of a specification part, which describes the problem at hand, and a solution part, which describes the solution used to solve this problem. New problems are solved by retrieving a case whose specification is similar to the current target problem and then adapting its solution to fit the target situation. For example, CLAVIER [39] is a case-based reasoning system used by Lockheed to assist in determining the layout of materials to be cured in an autoclave (i.e., a large convection oven used, in this
Case-Base Similarity Target Specification Knowledg (parts list spec ○○◎ ◇◇△ Retrieve ○○口 Ct Learn Adapt ○○囗 ◎◎◎ Adaptation Target Solution Rules (parts layout Fig. 1l. 1. CLAVIER uses CBR to design layout configurations for a set of parts to be cured in an autoclave. This is a complex layout task that does not lend itself to a traditional knowledge based approach. However a case base of high-quality past layouts can be readily assembled. New layouts for a target parts-list can then be produced by retrieving a case with a similar parts-list and adapting its layout. If successful this new layout can then be learned by storing it in the case base as a new case case, for the curing of composite materials for aerospace applications). CLAVIER has the job of designing a good layout-one that will maximise autoclave throughput for a new parts-list. The rules for determining a good layout are not well understood but previous layouts that have proved to be successful are readily available. CLAVIER uses these previous layout examples as the cases in its case base. Each case is made up of a parts-list (its specification) and the particular layout used(its solution). Nev layouts for a new parts-list are determined by matching the new parts-list against these cases and adapting the layout solution used by the most similar case; see Figure 11.1 CLAVIER has been a huge practical success and has been in use for a number of years by Lockheed, virtually eliminating the production of low-quality parts that must crapped, and saving thousands of dollars each month Case-based recommenders borrow heavily from the core concepts of retrieval and milarity in case-based reasoning. Items or products are represented as cases and rec- ommendations are generated by retrieving those that are most similar to a users
11 Case-Based Recommendation 345 Case-Base Target Specification (parts list) Retrieve Adapt Target Solution (parts layout) Learn Similarity Knowledge Adaptation Rules spect c1,…cn cbest sol ct t ct Fig. 11.1. CLAVIER uses CBR to design layout configurations for a set of parts to be cured in an autoclave. This is a complex layout task that does not lend itself to a traditional knowledgebased approach. However a case base of high-quality past layouts can be readily assembled. New layouts for a target parts-list can then be produced by retrieving a case with a similar parts-list and adapting its layout. If successful this new layout can then be learned by storing it in the case base as a new case. case, for the curing of composite materials for aerospace applications). CLAVIER has the job of designing a good layout—one that will maximise autoclave throughput— for a new parts-list. The rules for determining a good layout are not well understood but previous layouts that have proved to be successful are readily available. CLAVIER uses these previous layout examples as the cases in its case base. Each case is made up of a parts-list (its specification) and the particular layout used (its solution). New layouts for a new parts-list are determined by matching the new parts-list against these cases and adapting the layout solution used by the most similar case; see Figure 11.1. CLAVIER has been a huge practical success and has been in use for a number of years by Lockheed, virtually eliminating the production of low-quality parts that must be scrapped, and saving thousands of dollars each month. Case-based recommenders borrow heavily from the core concepts of retrieval and similarity in case-based reasoning. Items or products are represented as cases and recommendations are generated by retrieving those cases that are most similar to a user’s
46 B. Smyth query or profile. The simplest form of case-based recommendation is presented in Fig- ure 11.2. In this figure we use the example of a digital camera recommender system, with the product case base made up of detailed descriptions of individual digital cam- eras. When the user submits a target query--in this instance providing a relatively vague description of their requirements in relation to camera price and pixel resolution--they re presented with a ranked list of k recommendations which represent the top k most milar cases that match the target query As a form of content-based recommendation Product Case-Base Knowledge Case Price: 1000 Pixel: 6 C, sim(t, C,)1, CIsm(t, c,)), 11. 2. In its simplest form a case-based recommendation system will nk product by comparing the user's target query to the descriptions of products In Its ca base using similarity knowledge to identify products that are close matches to the target query (see, for example, [5, 26, 63, 78, 94] and also Chapter 10 [69] of this book) case-base recommenders generate their recommendations by looking to the item descriptions, with items suggested because they have similar descriptions to the user's query. There are two important ways in which case-based recommender systems can be distinguished from other types of content-based systems: (1)the manner in which products are rep- resented; and (2) the way in which product similarity is assessed. Both of these will be discussed in detail in the following sections. 11.2.1 Case Representation Normally content-based recommender systems operate in situations where content items are represented in an unstructured or semi-structured manner. For example, the News Dude content-recommender. which recommends news articles to users. assumes
346 B. Smyth query or profile. The simplest form of case-based recommendation is presented in Figure 11.2. In this figure we use the example of a digital camera recommender system, with the product case base made up of detailed descriptions of individual digital cameras. When the user submits a target query—in this instance providing a relatively vague description of their requirements in relation to camera price and pixel resolution—they are presented with a ranked list of k recommendations which represent the top k most similar cases that match the target query. As a form of content-based recommendation Product Case-Base Price: 1000 Pixel: 6 Target Query, t Case Retrieval Similarity Knowledge c1,…cn Product Recommendations c1 {sim(t, c1)}, : ck {sim(t, c1)}, c1,…ck Fig. 11.2. In its simplest form a case-based recommendation system will retrieve and rank product suggestions by comparing the user’s target query to the descriptions of products stored in its case base using similarity knowledge to identify products that are close matches to the target query. (see, for example, [5, 26, 63, 78, 94] and also Chapter 10 [69] of this book) case-based recommenders generate their recommendations by looking to the item descriptions, with items suggested because they have similar descriptions to the user’s query. There are two important ways in which case-based recommender systems can be distinguished from other types of content-based systems: (1) the manner in which products are represented; and (2) the way in which product similarity is assessed. Both of these will be discussed in detail in the following sections. 11.2.1 Case Representation Normally content-based recommender systems operate in situations where content items are represented in an unstructured or semi-structured manner. For example, the NewsDude content-recommender, which recommends news articles to users, assumes